Code
library(readr)
library(ggplot2)
library(sf)
library(rnaturalearth)
library(rnaturalearthdata)
library(dplyr)
library(gridExtra)
library(biscale)
library(colorspace)
library(grid)
library(jsonlite)
library(here)CAPTAIN workflow continental vs highseas
Visualise CAPTAIN results
R libraries
FUSE : conservation prioritiy maps
Budget: 0.3
Code
# Read both RDS files from the Data folder
continental_data_FUSE_03 <- readRDS(here::here("Data/FUSE_continental_full_results_averaged_budget0.3_replicates10.rds"))
high_seas_data_FUSE_03 <- readRDS(here::here("Data/FUSE_full_highseas_results_averaged_budget0.3_replicates10.rds"))
# Get world map data
world <- ne_countries(scale = "medium", returnclass = "sf")
# Define the McBryde-Thomas 2 projection
mcbryde_thomas_2 <- "+proj=mbt_s"
# Transform both datasets to sf objects and project
continental_sf_FUSE_03 <- st_as_sf(continental_data_FUSE_03, coords = c("Longitude", "Latitude"), crs = 4326) %>%
st_transform(crs = mcbryde_thomas_2)
high_seas_sf_FUSE_03 <- st_as_sf(high_seas_data_FUSE_03, coords = c("Longitude", "Latitude"), crs = 4326) %>%
st_transform(crs = mcbryde_thomas_2)
# Combine the datasets
combined_sf_FUSE_03 <- rbind(
mutate(continental_sf_FUSE_03, Region = "Continental Waters"),
mutate(high_seas_sf_FUSE_03, Region = "High Seas")
)
# Project the world map
world_projected_FUSE_03 <- st_transform(world, crs = mcbryde_thomas_2)
# Create the globe bounding box
globe_bbox <- rbind(c(-180, -90), c(-180, 90),
c(180, 90), c(180, -90), c(-180, -90))
# Create the globe border
globe_border <- st_polygon(list(globe_bbox)) %>%
st_sfc(crs = 4326) %>%
st_sf(data.frame(rgn = 'globe', geom = .)) %>%
smoothr::densify(max_distance = 0.5) %>%
st_transform(crs = mcbryde_thomas_2)
# Create base theme
my_theme <- theme_minimal() +
theme(
legend.position = "bottom",
legend.direction = "horizontal",
legend.box = "vertical",
legend.margin = margin(t = 20, r = 0, b = 0, l = 0),
legend.title = element_text(margin = margin(b = 10)),
panel.background = element_rect(fill = "white", color = NA),
plot.background = element_rect(fill = "white", color = NA),
panel.grid = element_blank()
)
# 1. Continental Waters Plot
continental_plot_FUSE_03 <- ggplot() +
geom_sf(data = continental_sf_FUSE_03, aes(color = Priority), size = 0.5, alpha = 0.7) +
geom_sf(data = world_projected_FUSE_03, fill = "lightgrey", color = "lightgrey", size = 0.1) +
geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
scale_color_gradientn(
colors = c("white", "yellow", "darkblue"),
values = c(0, 0.5, 1),
name = "Priority",
guide = guide_colorbar(barwidth = 20, barheight = 0.5,
title.position = "top", title.hjust = 0.5)
) +
labs(title = "Conservation Priorities in Continental Waters",
subtitle = "Index: FUSE, Budget: 0.3, Replicates: 10",
x = NULL, y = NULL) +
my_theme
# 2. High Seas Plot
high_seas_plot_FUSE_03 <- ggplot() +
geom_sf(data = high_seas_sf_FUSE_03, aes(color = Priority), size = 0.5, alpha = 0.7) +
geom_sf(data = world_projected_FUSE_03, fill = "lightgrey", color = "lightgrey", size = 0.1) +
geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
scale_color_gradientn(
colors = c("white", "yellow", "darkblue"),
values = c(0, 0.5, 1),
name = "Priority",
guide = guide_colorbar(barwidth = 20, barheight = 0.5,
title.position = "top", title.hjust = 0.5)
) +
labs(title = "Conservation Priorities in High Seas",
subtitle = "Index: FUSE, Budget: 0.3, Replicates: 10",
x = NULL, y = NULL) +
my_theme
# Combined Plot (modified)
combined_plot_FUSE_03 <- ggplot() +
geom_sf(data = combined_sf_FUSE_03,
aes(color = Priority),
size = 0.5,
alpha = 0.7) +
geom_sf(data = world_projected_FUSE_03, fill = "lightgrey", color = "lightgrey", size = 0.1) +
geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
scale_color_gradientn(
colors = c("white", "yellow", "darkblue"),
values = c(0, 0.5, 1),
name = "Priority",
guide = guide_colorbar(barwidth = 20, barheight = 0.5,
title.position = "top", title.hjust = 0.5)
) +
labs(title = "Combined Conservation Priorities",
subtitle = "Continental Waters and High Seas\nIndex: FUSE, Budget: 0.3, Replicates: 10",
x = NULL, y = NULL) +
my_theme
# Display all three plots
#library(patchwork)
continental_plot_FUSE_03 
Code
high_seas_plot_FUSE_03 
Code
combined_plot_FUSE_03
Budget: 0.1
Code
# Read both RDS files from the Data folder
continental_data_FUSE_01 <- readRDS(here::here("Data/FUSE_full_results_continental_averaged_budget0.1_replicates10.rds"))
high_seas_data_FUSE_01 <- readRDS(here::here("Data/FUSE_full_highseas_results_averaged_budget0.1_replicates10.rds"))
# Get world map data
world <- ne_countries(scale = "medium", returnclass = "sf")
# Define the McBryde-Thomas 2 projection
mcbryde_thomas_2 <- "+proj=mbt_s"
# Transform both datasets to sf objects and project
continental_sf_FUSE_01 <- st_as_sf(continental_data_FUSE_01, coords = c("Longitude", "Latitude"), crs = 4326) %>%
st_transform(crs = mcbryde_thomas_2)
high_seas_sf_FUSE_01 <- st_as_sf(high_seas_data_FUSE_01, coords = c("Longitude", "Latitude"), crs = 4326) %>%
st_transform(crs = mcbryde_thomas_2)
# Combine the datasets
combined_sf_FUSE_01 <- rbind(
mutate(continental_sf_FUSE_01, Region = "Continental Waters"),
mutate(high_seas_sf_FUSE_01, Region = "High Seas")
)
# Project the world map
world_projected_FUSE_01 <- st_transform(world, crs = mcbryde_thomas_2)
# Create the globe bounding box
globe_bbox <- rbind(c(-180, -90), c(-180, 90),
c(180, 90), c(180, -90), c(-180, -90))
# Create the globe border
globe_border <- st_polygon(list(globe_bbox)) %>%
st_sfc(crs = 4326) %>%
st_sf(data.frame(rgn = 'globe', geom = .)) %>%
smoothr::densify(max_distance = 0.5) %>%
st_transform(crs = mcbryde_thomas_2)
# Create base theme
my_theme <- theme_minimal() +
theme(
legend.position = "bottom",
legend.direction = "horizontal",
legend.box = "vertical",
legend.margin = margin(t = 20, r = 0, b = 0, l = 0),
legend.title = element_text(margin = margin(b = 10)),
panel.background = element_rect(fill = "white", color = NA),
plot.background = element_rect(fill = "white", color = NA),
panel.grid = element_blank()
)
# 1. Continental Waters Plot
continental_plot_FUSE_01 <- ggplot() +
geom_sf(data = continental_sf_FUSE_01, aes(color = Priority), size = 0.5, alpha = 0.7) +
geom_sf(data = world_projected_FUSE_01, fill = "lightgrey", color = "lightgrey", size = 0.1) +
geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
scale_color_gradientn(
colors = c("white", "yellow", "darkblue"),
values = c(0, 0.5, 1),
name = "Priority",
guide = guide_colorbar(barwidth = 20, barheight = 0.5,
title.position = "top", title.hjust = 0.5)
) +
labs(title = "Conservation Priorities in Continental Waters",
subtitle = "Index: FUSE, Budget: 0.1, Replicates: 10",
x = NULL, y = NULL) +
my_theme
# 2. High Seas Plot
high_seas_plot_FUSE_01 <- ggplot() +
geom_sf(data = high_seas_sf_FUSE_01, aes(color = Priority), size = 0.5, alpha = 0.7) +
geom_sf(data = world_projected_FUSE_01, fill = "lightgrey", color = "lightgrey", size = 0.1) +
geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
scale_color_gradientn(
colors = c("white", "yellow", "darkblue"),
values = c(0, 0.5, 1),
name = "Priority",
guide = guide_colorbar(barwidth = 20, barheight = 0.5,
title.position = "top", title.hjust = 0.5)
) +
labs(title = "Conservation Priorities in High Seas",
subtitle = "Index: FUSE, Budget: 0.1, Replicates: 10",
x = NULL, y = NULL) +
my_theme
# Combined Plot (modified)
combined_plot_FUSE_01 <- ggplot() +
geom_sf(data = combined_sf_FUSE_01,
aes(color = Priority),
size = 0.5,
alpha = 0.7) +
geom_sf(data = world_projected_FUSE_01, fill = "lightgrey", color = "lightgrey", size = 0.1) +
geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
scale_color_gradientn(
colors = c("white", "yellow", "darkblue"),
values = c(0, 0.5, 1),
name = "Priority",
guide = guide_colorbar(barwidth = 20, barheight = 0.5,
title.position = "top", title.hjust = 0.5)
) +
labs(title = "Combined Conservation Priorities",
subtitle = "Continental Waters and High Seas\nIndex: FUSE, Budget: 0.1, Replicates: 10",
x = NULL, y = NULL) +
my_theme
# Display all three plots
#library(patchwork)
continental_plot_FUSE_01 
Code
high_seas_plot_FUSE_01 
Code
combined_plot_FUSE_01
EDGE2 : conservation prioritiy maps
Budget: 0.3
Code
# Read both RDS files from the Data folder
continental_data_EDGE2_03 <- readRDS(here::here("Data/EDGE2_full_results_continental_averaged_budget0.3_replicates10.rds"))
high_seas_data_EDGE2_03 <- readRDS(here::here("Data/EDGE2_full_highseas_results_averaged_budget0.3_replicates10.rds"))
# Get world map data
world <- ne_countries(scale = "medium", returnclass = "sf")
# Define the McBryde-Thomas 2 projection
mcbryde_thomas_2 <- "+proj=mbt_s"
# Transform both datasets to sf objects and project
continental_sf_EDGE2_03 <- st_as_sf(continental_data_EDGE2_03, coords = c("Longitude", "Latitude"), crs = 4326) %>%
st_transform(crs = mcbryde_thomas_2)
high_seas_sf_EDGE2_03 <- st_as_sf(high_seas_data_EDGE2_03, coords = c("Longitude", "Latitude"), crs = 4326) %>%
st_transform(crs = mcbryde_thomas_2)
# Combine the datasets
combined_sf_EDGE2_03 <- rbind(
mutate(continental_sf_EDGE2_03, Region = "Continental Waters"),
mutate(high_seas_sf_EDGE2_03, Region = "High Seas")
)
# Project the world map
world_projected_EDGE2_03 <- st_transform(world, crs = mcbryde_thomas_2)
# Create the globe bounding box
globe_bbox <- rbind(c(-180, -90), c(-180, 90),
c(180, 90), c(180, -90), c(-180, -90))
# Create the globe border
globe_border <- st_polygon(list(globe_bbox)) %>%
st_sfc(crs = 4326) %>%
st_sf(data.frame(rgn = 'globe', geom = .)) %>%
smoothr::densify(max_distance = 0.5) %>%
st_transform(crs = mcbryde_thomas_2)
# Create base theme
my_theme <- theme_minimal() +
theme(
legend.position = "bottom",
legend.direction = "horizontal",
legend.box = "vertical",
legend.margin = margin(t = 20, r = 0, b = 0, l = 0),
legend.title = element_text(margin = margin(b = 10)),
panel.background = element_rect(fill = "white", color = NA),
plot.background = element_rect(fill = "white", color = NA),
panel.grid = element_blank()
)
# 1. Continental Waters Plot
continental_plot_EDGE2_03 <- ggplot() +
geom_sf(data = continental_sf_EDGE2_03, aes(color = Priority), size = 0.5, alpha = 0.7) +
geom_sf(data = world_projected_EDGE2_03, fill = "lightgrey", color = "lightgrey", size = 0.1) +
geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
scale_color_gradientn(
colors = c("white", "yellow", "darkblue"),
values = c(0, 0.5, 1),
name = "Priority",
guide = guide_colorbar(barwidth = 20, barheight = 0.5,
title.position = "top", title.hjust = 0.5)
) +
labs(title = "Conservation Priorities in Continental Waters",
subtitle = "Index: EDGE2, Budget: 0.3, Replicates: 10",
x = NULL, y = NULL) +
my_theme
# 2. High Seas Plot
high_seas_plot_EDGE2_03 <- ggplot() +
geom_sf(data = high_seas_sf_EDGE2_03, aes(color = Priority), size = 0.5, alpha = 0.7) +
geom_sf(data = world_projected_EDGE2_03, fill = "lightgrey", color = "lightgrey", size = 0.1) +
geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
scale_color_gradientn(
colors = c("white", "yellow", "darkblue"),
values = c(0, 0.5, 1),
name = "Priority",
guide = guide_colorbar(barwidth = 20, barheight = 0.5,
title.position = "top", title.hjust = 0.5)
) +
labs(title = "Conservation Priorities in High Seas",
subtitle = "Index: EDGE2, Budget: 0.3, Replicates: 10",
x = NULL, y = NULL) +
my_theme
# Combined Plot (modified)
combined_plot_EDGE2_03 <- ggplot() +
geom_sf(data = combined_sf_EDGE2_03,
aes(color = Priority),
size = 0.5,
alpha = 0.7) +
geom_sf(data = world_projected_EDGE2_03, fill = "lightgrey", color = "lightgrey", size = 0.1) +
geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
scale_color_gradientn(
colors = c("white", "yellow", "darkblue"),
values = c(0, 0.5, 1),
name = "Priority",
guide = guide_colorbar(barwidth = 20, barheight = 0.5,
title.position = "top", title.hjust = 0.5)
) +
labs(title = "Combined Conservation Priorities",
subtitle = "Continental Waters and High Seas\nIndex: EDGE2, Budget: 0.3, Replicates: 10",
x = NULL, y = NULL) +
my_theme
# Display all three plots
#library(patchwork)
continental_plot_EDGE2_03 
Code
high_seas_plot_EDGE2_03 
Code
combined_plot_EDGE2_03
Budget: 0.1
Code
# Read both RDS files from the Data folder
continental_data_EDGE2_01 <- readRDS(here::here("Data/EDGE2_full_results_continental_averaged_budget0.1_replicates10.rds"))
high_seas_data_EDGE2_01 <- readRDS(here::here("Data/EDGE2_full_highseas_results_averaged_budget0.1_replicates10.rds"))
# Get world map data
world <- ne_countries(scale = "medium", returnclass = "sf")
# Define the McBryde-Thomas 2 projection
mcbryde_thomas_2 <- "+proj=mbt_s"
# Transform both datasets to sf objects and project
continental_sf_EDGE2_01 <- st_as_sf(continental_data_EDGE2_01, coords = c("Longitude", "Latitude"), crs = 4326) %>%
st_transform(crs = mcbryde_thomas_2)
high_seas_sf_EDGE2_01 <- st_as_sf(high_seas_data_EDGE2_01, coords = c("Longitude", "Latitude"), crs = 4326) %>%
st_transform(crs = mcbryde_thomas_2)
# Combine the datasets
combined_sf_EDGE2_01 <- rbind(
mutate(continental_sf_EDGE2_01, Region = "Continental Waters"),
mutate(high_seas_sf_EDGE2_01, Region = "High Seas")
)
# Project the world map
world_projected_EDGE2_01 <- st_transform(world, crs = mcbryde_thomas_2)
# Create the globe bounding box
globe_bbox <- rbind(c(-180, -90), c(-180, 90),
c(180, 90), c(180, -90), c(-180, -90))
# Create the globe border
globe_border <- st_polygon(list(globe_bbox)) %>%
st_sfc(crs = 4326) %>%
st_sf(data.frame(rgn = 'globe', geom = .)) %>%
smoothr::densify(max_distance = 0.5) %>%
st_transform(crs = mcbryde_thomas_2)
# Create base theme
my_theme <- theme_minimal() +
theme(
legend.position = "bottom",
legend.direction = "horizontal",
legend.box = "vertical",
legend.margin = margin(t = 20, r = 0, b = 0, l = 0),
legend.title = element_text(margin = margin(b = 10)),
panel.background = element_rect(fill = "white", color = NA),
plot.background = element_rect(fill = "white", color = NA),
panel.grid = element_blank()
)
# 1. Continental Waters Plot
continental_plot_EDGE2_01 <- ggplot() +
geom_sf(data = continental_sf_EDGE2_01, aes(color = Priority), size = 0.5, alpha = 0.7) +
geom_sf(data = world_projected_EDGE2_01, fill = "lightgrey", color = "lightgrey", size = 0.1) +
geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
scale_color_gradientn(
colors = c("white", "yellow", "darkblue"),
values = c(0, 0.5, 1),
name = "Priority",
guide = guide_colorbar(barwidth = 20, barheight = 0.5,
title.position = "top", title.hjust = 0.5)
) +
labs(title = "Conservation Priorities in Continental Waters",
subtitle = "Index: EDGE2, Budget: 0.1, Replicates: 10",
x = NULL, y = NULL) +
my_theme
# 2. High Seas Plot
high_seas_plot_EDGE2_01 <- ggplot() +
geom_sf(data = high_seas_sf_EDGE2_01, aes(color = Priority), size = 0.5, alpha = 0.7) +
geom_sf(data = world_projected_EDGE2_01, fill = "lightgrey", color = "lightgrey", size = 0.1) +
geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
scale_color_gradientn(
colors = c("white", "yellow", "darkblue"),
values = c(0, 0.5, 1),
name = "Priority",
guide = guide_colorbar(barwidth = 20, barheight = 0.5,
title.position = "top", title.hjust = 0.5)
) +
labs(title = "Conservation Priorities in High Seas",
subtitle = "Index: EDGE2, Budget: 0.1, Replicates: 10",
x = NULL, y = NULL) +
my_theme
# Combined Plot (modified)
combined_plot_EDGE2_01 <- ggplot() +
geom_sf(data = combined_sf_EDGE2_01,
aes(color = Priority),
size = 0.5,
alpha = 0.7) +
geom_sf(data = world_projected_EDGE2_01, fill = "lightgrey", color = "lightgrey", size = 0.1) +
geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
scale_color_gradientn(
colors = c("white", "yellow", "darkblue"),
values = c(0, 0.5, 1),
name = "Priority",
guide = guide_colorbar(barwidth = 20, barheight = 0.5,
title.position = "top", title.hjust = 0.5)
) +
labs(title = "Combined Conservation Priorities",
subtitle = "Continental Waters and High Seas\nIndex: EDGE2, Budget: 0.1, Replicates: 10",
x = NULL, y = NULL) +
my_theme
# Display all three plots
#library(patchwork)
continental_plot_EDGE2_01 
Code
high_seas_plot_EDGE2_01 
Code
combined_plot_EDGE2_01
Congruence tests
Budget: 0.1
Code
# Spearman's rank correlation
# Calculate correlation between FUSE and EDGE2 priorities
congruence_FUSE_EDGE2_01 <- cor.test(combined_sf_FUSE_01$Priority,
combined_sf_EDGE2_01$Priority,
method = "spearman")
# Create a scatterplot to visualize the relationship
congruence_plot_01 <- ggplot(data.frame(
FUSE = combined_sf_FUSE_01$Priority,
EDGE2 = combined_sf_EDGE2_01$Priority
), aes(x = FUSE, y = EDGE2)) +
geom_point(alpha = 0.3) +
geom_smooth(method = "lm", color = "red") +
labs(
title = "Congruence between FUSE and EDGE2 Priorities",
subtitle = paste("Spearman's rho =",
round(congruence_FUSE_EDGE2_01$estimate, 3),
"\np-value <",
format.pval(congruence_FUSE_EDGE2_01$p.value, digits = 3)),
x = "FUSE Priority (Budget = 0.1)",
y = "EDGE2 Priority (Budget = 0.1)"
) +
theme_minimal()
# Display results
print(congruence_FUSE_EDGE2_01)
Spearman's rank correlation rho
data: combined_sf_FUSE_01$Priority and combined_sf_EDGE2_01$Priority
S = 1.3942e+14, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.6306786 Code
congruence_plot_01
Code
# Ovelaps test
# Create binary maps of high priority areas (≥ 0.8)
high_priority_FUSE_01 <- combined_sf_FUSE_01$Priority >= 0.8
high_priority_EDGE2_01 <- combined_sf_EDGE2_01$Priority >= 0.8
# Calculate overlaps
total_high_FUSE <- sum(high_priority_FUSE_01)
total_high_EDGE2 <- sum(high_priority_EDGE2_01)
overlap_areas <- sum(high_priority_FUSE_01 & high_priority_EDGE2_01)
# Calculate percentages
percent_overlap_FUSE <- (overlap_areas / total_high_FUSE) * 100
percent_overlap_EDGE2 <- (overlap_areas / total_high_EDGE2) * 100
# Print results
cat("Number of high-priority cells:\n",
"FUSE:", total_high_FUSE, "\n",
"EDGE2:", total_high_EDGE2, "\n",
"Overlap:", overlap_areas, "\n\n",
"Percentage of FUSE high-priority areas that overlap with EDGE2:", round(percent_overlap_FUSE, 1), "%\n",
"Percentage of EDGE2 high-priority areas that overlap with FUSE:", round(percent_overlap_EDGE2, 1), "%\n")Number of high-priority cells:
FUSE: 2130
EDGE2: 1951
Overlap: 1799
Percentage of FUSE high-priority areas that overlap with EDGE2: 84.5 %
Percentage of EDGE2 high-priority areas that overlap with FUSE: 92.2 %Code
# Function to calculate hotspot overlaps with significance testing using absolute threshold
calculate_priority_overlap <- function(index1, index2, threshold = 0.8, n_permutations = 999) {
# Identify high priority areas (>= threshold)
priority1 <- index1$Priority >= threshold
priority2 <- index2$Priority >= threshold
# Calculate observed overlap
Ni <- sum(priority1) # Number of high priority areas in index1
Nj <- sum(priority2) # Number of high priority areas in index2
NT <- length(priority1) # Total number of cells
Oo <- sum(priority1 & priority2) # Observed overlap
Oe <- (Ni * Nj) / NT # Expected overlap under independence
# Randomization procedure
random_overlaps <- numeric(n_permutations)
for(i in 1:n_permutations) {
random_priority2 <- sample(priority2) # Randomly permute second index
random_overlaps[i] <- sum(priority1 & random_priority2)
}
# Calculate p-value
if(Oo > Oe) {
p_value <- sum(random_overlaps >= Oo) / n_permutations
} else {
p_value <- sum(random_overlaps <= Oo) / n_permutations
}
# Calculate percentage of observed overlap
percent_overlap <- (Oo / min(Ni, Nj)) * 100
return(list(
observed_overlap = Oo,
expected_overlap = Oe,
total_priority_index1 = Ni,
total_priority_index2 = Nj,
percent_overlap = percent_overlap,
p_value = p_value
))
}
# Run the analysis for 0.1 budget level with 0.8 threshold
results_01 <- calculate_priority_overlap(
combined_sf_FUSE_01,
combined_sf_EDGE2_01,
threshold = 0.9
)
# Print results
cat("Results for 0.1 budget level (Priority >= 0.8):\n",
"\nNumber of high priority areas:",
"\nFUSE:", results_01$total_priority_index1,
"\nEDGE2:", results_01$total_priority_index2,
"\n\nOverlap analysis:",
"\nObserved overlap:", results_01$observed_overlap,
"\nExpected overlap:", round(results_01$expected_overlap, 2),
"\nPercentage overlap:", round(results_01$percent_overlap, 1), "%",
"\np-value:", format.pval(results_01$p_value, digits = 3))Results for 0.1 budget level (Priority >= 0.8):
Number of high priority areas:
FUSE: 1812
EDGE2: 1718
Overlap analysis:
Observed overlap: 1632
Expected overlap: 23.7
Percentage overlap: 95 %
p-value: <2e-16Budget: 0.3
Code
# Calculate correlation between FUSE and EDGE2 priorities at 0.3 budget
congruence_FUSE_EDGE2_03 <- cor.test(combined_sf_FUSE_03$Priority,
combined_sf_EDGE2_03$Priority,
method = "spearman")
# Create a scatterplot to visualize the relationship
congruence_plot_03 <- ggplot(data.frame(
FUSE = combined_sf_FUSE_03$Priority,
EDGE2 = combined_sf_EDGE2_03$Priority
), aes(x = FUSE, y = EDGE2)) +
geom_point(alpha = 0.3) +
geom_smooth(method = "lm", color = "red") +
labs(
title = "Congruence between FUSE and EDGE2 Priorities",
subtitle = paste("Spearman's rho =",
round(congruence_FUSE_EDGE2_03$estimate, 3),
"\np-value <",
format.pval(congruence_FUSE_EDGE2_03$p.value, digits = 3)),
x = "FUSE Priority (Budget = 0.3)",
y = "EDGE2 Priority (Budget = 0.3)"
) +
theme_minimal()
# Display results
print(congruence_FUSE_EDGE2_03)
Spearman's rank correlation rho
data: combined_sf_FUSE_03$Priority and combined_sf_EDGE2_03$Priority
S = 7.8895e+13, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.7910035 Code
congruence_plot_03
Code
# Calculate priority overlap
results_03 <- calculate_priority_overlap(
combined_sf_FUSE_03,
combined_sf_EDGE2_03,
threshold = 0.9
)
# Print results
cat("Results for 0.3 budget level (Priority >= 0.8):\n",
"\nNumber of high priority areas:",
"\nFUSE:", results_03$total_priority_index1,
"\nEDGE2:", results_03$total_priority_index2,
"\n\nOverlap analysis:",
"\nObserved overlap:", results_03$observed_overlap,
"\nExpected overlap:", round(results_03$expected_overlap, 2),
"\nPercentage overlap:", round(results_03$percent_overlap, 1), "%",
"\np-value:", format.pval(results_03$p_value, digits = 3))Results for 0.3 budget level (Priority >= 0.8):
Number of high priority areas:
FUSE: 3470
EDGE2: 3543
Overlap analysis:
Observed overlap: 2709
Expected overlap: 93.62
Percentage overlap: 78.1 %
p-value: <2e-16FUSE and EDGE2 : conservation prioritiy bivariate maps
Budget: 0.3
Code
# Read all RDS files
edge_continental <- readRDS(here::here("Data/EDGE2_full_results_continental_averaged_budget0.3_replicates10.rds"))
edge_highseas <- readRDS(here::here("Data/EDGE2_full_highseas_results_averaged_budget0.3_replicates10.rds"))
fuse_continental <- readRDS(here::here("Data/FUSE_continental_full_results_averaged_budget0.3_replicates10.rds"))
fuse_highseas <- readRDS(here::here("Data/FUSE_full_highseas_results_averaged_budget0.3_replicates10.rds"))
# Get world map data and set projection
world <- ne_countries(scale = "medium", returnclass = "sf")
mcbryde_thomas_2 <- "+proj=mbt_s"
# Function to process and combine data
process_data <- function(edge_data, fuse_data) {
combined_data <- edge_data %>%
rename(EDGE_Priority = Priority) %>%
left_join(fuse_data %>% rename(FUSE_Priority = Priority),
by = c("Longitude", "Latitude"))
# Normalize priorities to 0-1 range
combined_data <- combined_data %>%
mutate(
EDGE_Priority_Norm = (EDGE_Priority - min(EDGE_Priority)) / (max(EDGE_Priority) - min(EDGE_Priority)),
FUSE_Priority_Norm = (FUSE_Priority - min(FUSE_Priority)) / (max(FUSE_Priority) - min(FUSE_Priority))
)
# Transform to sf object
data_sf <- st_as_sf(combined_data, coords = c("Longitude", "Latitude"), crs = 4326) %>%
st_transform(crs = mcbryde_thomas_2)
return(data_sf)
}
# Process continental and high seas data
continental_sf <- process_data(edge_continental, fuse_continental)
highseas_sf <- process_data(edge_highseas, fuse_highseas)
# Combine continental and high seas data for the combined map
combined_sf <- rbind(continental_sf, highseas_sf)
# Project the world map
world_projected <- st_transform(world, crs = mcbryde_thomas_2)
# Create color palette
map_pal_raw <- bi_pal(pal = 'PurpleOr', dim = 4, preview = FALSE)
map_pal_mtx <- matrix(map_pal_raw, nrow = 4, ncol = 4)
map_pal_mtx[3, ] <- colorspace::lighten(map_pal_mtx[3, ], .1)
map_pal_mtx[2, ] <- colorspace::lighten(map_pal_mtx[2, ], .2)
map_pal_mtx[1, ] <- colorspace::lighten(map_pal_mtx[1, ], .3)
map_pal_mtx[ , 3] <- colorspace::lighten(map_pal_mtx[ , 3], .1)
map_pal_mtx[ , 2] <- colorspace::lighten(map_pal_mtx[ , 2], .2)
map_pal_mtx[ , 1] <- colorspace::lighten(map_pal_mtx[ , 1], .3)
map_pal_mtx[1, 1] <- '#ffffee'
map_pal <- as.vector(map_pal_mtx) %>% setNames(names(map_pal_raw))
# Color mapping function
get_color <- function(edge, fuse) {
edge_class <- cut(edge, breaks = c(-Inf, 0.25, 0.5, 0.75, Inf), labels = 1:4)
fuse_class <- cut(fuse, breaks = c(-Inf, 0.25, 0.5, 0.75, Inf), labels = 1:4)
return(map_pal[(as.numeric(fuse_class)-1)*4 + as.numeric(edge_class)])
}
# Apply colors to all datasets
continental_sf$new_color <- mapply(get_color, continental_sf$EDGE_Priority_Norm, continental_sf$FUSE_Priority_Norm)
highseas_sf$new_color <- mapply(get_color, highseas_sf$EDGE_Priority_Norm, highseas_sf$FUSE_Priority_Norm)
combined_sf$new_color <- mapply(get_color, combined_sf$EDGE_Priority_Norm, combined_sf$FUSE_Priority_Norm)
# Create plot function
create_bivariate_plot <- function(data_sf, title) {
ggplot() +
geom_sf(data = data_sf, aes(color = new_color), size = 0.1, alpha = 1) +
geom_sf(data = world_projected, fill = "lightgray", color = "lightgray") +
geom_sf(data = globe_border, fill = NA, color = "grey70", size = 0.5) +
scale_color_identity() +
coord_sf() +
theme_minimal() +
labs(title = title,
x = NULL, y = NULL) +
theme(plot.title = element_text(hjust = 0.5))
}
# Create legend
legend_plot <- bi_legend(pal = map_pal, dim = 4,
xlab = 'EDGE2',
ylab = 'FUSE')
# Create the individual plots
continental_bivariate <- create_bivariate_plot(continental_sf, "Continental Waters: EDGE2 vs FUSE Priorities")
highseas_bivariate <- create_bivariate_plot(highseas_sf, "High Seas: EDGE2 vs FUSE Priorities")
combined_bivariate_03 <- create_bivariate_plot(combined_sf, "Combined Waters: EDGE2 vs FUSE Priorities; Budget: 0.3")
# Create the map for the ms
# Modify the create_bivariate_plot function for this specific case
combined_bivariate_03_ms <- ggplot() +
geom_sf(data = combined_sf, aes(color = new_color), size = 0.1, alpha = 1) +
geom_sf(data = world_projected, fill = "lightgray", color = "lightgray") +
geom_sf(data = globe_border, fill = NA, color = "grey70", size = 0.5) +
scale_color_identity() +
coord_sf() +
theme_minimal() +
labs(x = NULL, y = NULL) +
annotate("text", x = -Inf, y = Inf, label = "(B)",
hjust = -1, vjust = 2, size = 6, fontface = "bold") +
theme(panel.grid = element_blank(),
plot.margin = margin(t = 10, r = 10, b = 10, l = 10, unit = "pt"))
# Arrange plots with shared legend
layout <- rbind(
c(1, 1, 1),
c(2, 2, 2),
c(3, 3, 3),
c(4, 4, 4)
)
# Create legend with larger text
legend_plot <- bi_legend(pal = map_pal, dim = 4,
xlab = 'EDGE2',
ylab = 'FUSE',
size = 2) + # Base size for the legend elements
theme(
axis.title = element_text(size = 18, face = "bold"), # Larger axis titles
axis.text = element_blank(), # Larger axis text
legend.text = element_text(size = 12), # Larger legend text
legend.title = element_text(size = 14, face = "bold") # Larger legend title
)
# Keep the rest of your grid.arrange code the same
combined_plot <- grid.arrange(
continental_bivariate,
highseas_bivariate,
combined_bivariate_03,
legend_plot,
layout_matrix = layout,
heights = c(0.32, 0.32, 0.32, 0.15),
widths = unit(c(15, 15, 15), "inches"),
top = textGrob("Bivariate Maps of EDGE2 and FUSE Priorities",
gp = gpar(fontsize = 16, font = 2))
)
Code
# Display the combined plot
#print(combined_plot)
# Save the plot if needed
# ggsave("bivariate_priority_maps_all.png", combined_plot, width = 15, height = 16, dpi = 300)Budget: 0.3 with more bins
Code
# Read all RDS files
edge_continental <- readRDS(here::here("Data/EDGE2_full_results_continental_averaged_budget0.3_replicates10.rds"))
edge_highseas <- readRDS(here::here("Data/EDGE2_full_highseas_results_averaged_budget0.3_replicates10.rds"))
fuse_continental <- readRDS(here::here("Data/FUSE_continental_full_results_averaged_budget0.3_replicates10.rds"))
fuse_highseas <- readRDS(here::here("Data/FUSE_full_highseas_results_averaged_budget0.3_replicates10.rds"))
# Get world map data and set projection
world <- ne_countries(scale = "medium", returnclass = "sf")
mcbryde_thomas_2 <- "+proj=mbt_s"
# Function to process and combine data
process_data <- function(edge_data, fuse_data) {
combined_data <- edge_data %>%
rename(EDGE_Priority = Priority) %>%
left_join(fuse_data %>% rename(FUSE_Priority = Priority),
by = c("Longitude", "Latitude"))
# Normalize priorities to 0-1 range
combined_data <- combined_data %>%
mutate(
EDGE_Priority_Norm = (EDGE_Priority - min(EDGE_Priority)) / (max(EDGE_Priority) - min(EDGE_Priority)),
FUSE_Priority_Norm = (FUSE_Priority - min(FUSE_Priority)) / (max(FUSE_Priority) - min(FUSE_Priority))
)
# Transform to sf object
data_sf <- st_as_sf(combined_data, coords = c("Longitude", "Latitude"), crs = 4326) %>%
st_transform(crs = mcbryde_thomas_2)
return(data_sf)
}
# Process continental and high seas data
continental_sf <- process_data(edge_continental, fuse_continental)
highseas_sf <- process_data(edge_highseas, fuse_highseas)
# Combine continental and high seas data for the combined map
combined_sf <- rbind(continental_sf, highseas_sf)
# Project the world map
world_projected <- st_transform(world, crs = mcbryde_thomas_2)
# Create custom 8x8 color palette function
create_custom_bivariate_palette <- function(n = 8) {
# Create color gradients
purple_colors <- colorRampPalette(c("#f7f7f7", "#af8dc3", "#7b3294"))(n)
orange_colors <- colorRampPalette(c("#f7f7f7", "#fdb863", "#e66101"))(n)
# Create the matrix
pal_matrix <- matrix(NA, nrow = n, ncol = n)
# Fill the matrix with color blends
for(i in 1:n) {
for(j in 1:n) {
# Blend the colors
color1 <- col2rgb(purple_colors[i])
color2 <- col2rgb(orange_colors[j])
# Mix the colors with varying weights
mixed_color <- rgb(
(color1[1] + color2[1])/2,
(color1[2] + color2[2])/2,
(color1[3] + color2[3])/2,
maxColorValue = 255
)
pal_matrix[i,j] <- mixed_color
}
}
# Apply lightening effect
for(i in 1:n) {
for(j in 1:n) {
light_factor <- (n - i) * 0.1 + (n - j) * 0.1
if(i != n || j != n) { # Don't lighten the darkest corner
pal_matrix[i,j] <- colorspace::lighten(pal_matrix[i,j], light_factor)
}
}
}
# Set the lightest corner
pal_matrix[1,1] <- "#ffffee"
# Convert matrix to vector
pal_vector <- as.vector(pal_matrix)
names(pal_vector) <- paste0("c", 1:(n*n))
return(list(
palette = pal_vector,
matrix = pal_matrix
))
}
# Create the custom palette
custom_pal <- create_custom_bivariate_palette(8)
map_pal <- custom_pal$palette
map_pal_mtx <- custom_pal$matrix
# Modified color mapping function for 8 bins
get_color <- function(edge, fuse) {
edge_breaks <- seq(0, 1, length.out = 9) # Creates 8 bins
fuse_breaks <- seq(0, 1, length.out = 9) # Creates 8 bins
edge_class <- cut(edge, breaks = edge_breaks, labels = 1:8, include.lowest = TRUE)
fuse_class <- cut(fuse, breaks = fuse_breaks, labels = 1:8, include.lowest = TRUE)
return(map_pal[(as.numeric(fuse_class)-1)*8 + as.numeric(edge_class)])
}
# Apply colors to all datasets
continental_sf$new_color <- mapply(get_color, continental_sf$EDGE_Priority_Norm, continental_sf$FUSE_Priority_Norm)
highseas_sf$new_color <- mapply(get_color, highseas_sf$EDGE_Priority_Norm, highseas_sf$FUSE_Priority_Norm)
combined_sf$new_color <- mapply(get_color, combined_sf$EDGE_Priority_Norm, combined_sf$FUSE_Priority_Norm)
# Create plot function
create_bivariate_plot <- function(data_sf, title) {
ggplot() +
geom_sf(data = data_sf, aes(color = new_color), size = 0.1, alpha = 1) +
geom_sf(data = world_projected, fill = "lightgray", color = "lightgray") +
geom_sf(data = globe_border, fill = NA, color = "grey70", size = 0.5) +
scale_color_identity() +
coord_sf() +
theme_minimal() +
labs(title = title,
x = NULL, y = NULL) +
theme(plot.title = element_text(hjust = 0.5))
}
# Create the individual plots
continental_bivariate <- create_bivariate_plot(continental_sf, "Continental Waters: EDGE2 vs FUSE Priorities")
highseas_bivariate <- create_bivariate_plot(highseas_sf, "High Seas: EDGE2 vs FUSE Priorities")
combined_bivariate_03 <- create_bivariate_plot(combined_sf, "Combined Waters: EDGE2 vs FUSE Priorities; Budget: 0.3")
# Create the map for the ms
combined_bivariate_03_ms <- ggplot() +
geom_sf(data = combined_sf, aes(color = new_color), size = 0.1, alpha = 1) +
geom_sf(data = world_projected, fill = "lightgray", color = "lightgray") +
geom_sf(data = globe_border, fill = NA, color = "grey70", size = 0.5) +
scale_color_identity() +
coord_sf() +
theme_minimal() +
labs(x = NULL, y = NULL) +
annotate("text", x = -Inf, y = Inf, label = "(B)",
hjust = -1, vjust = 2, size = 6, fontface = "bold") +
theme(panel.grid = element_blank(),
plot.margin = margin(t = 10, r = 10, b = 10, l = 10, unit = "pt"))
# Create custom legend function for 8x8 grid
create_custom_legend <- function() {
# Create a data frame for the legend
legend_data <- expand.grid(
x = 1:8,
y = 1:8
)
legend_data$color <- as.vector(map_pal_mtx)
# Create the legend plot
legend_plot <- ggplot(legend_data, aes(x = x, y = y, fill = color)) +
geom_tile() +
scale_fill_identity() +
labs(x = "EDGE2", y = "FUSE") +
theme_minimal() +
theme(
axis.title = element_text(size = 12, face = "bold"),
axis.text = element_blank(),
panel.grid = element_blank(),
plot.margin = margin(t = 5, r = 5, b = 5, l = 5),
aspect.ratio = 1 # Force square aspect ratio
) +
coord_fixed() # Ensure square tiles
return(legend_plot)
}
# Create the legend
legend_plot <- create_custom_legend()
# Layout for plots with smaller legend
layout <- rbind(
c(1, 1, 1, 1),
c(2, 2, 2, 2),
c(3, 3, 3, 3),
c(NA, 4, 4, NA) # This centers the legend and makes it smaller
)
# Arrange plots with shared legend
combined_plot <- grid.arrange(
continental_bivariate,
highseas_bivariate,
combined_bivariate_03,
legend_plot,
layout_matrix = layout,
heights = c(0.3, 0.3, 0.3, 0.15),
widths = c(0.25, 0.25, 0.25, 0.25),
top = textGrob("Bivariate Maps of EDGE2 and FUSE Priorities",
gp = gpar(fontsize = 16, font = 2))
)
Code
# Display the combined plot
print(combined_plot)TableGrob (5 x 4) "arrange": 5 grobs
z cells name grob
1 1 (2-2,1-4) arrange gtable[layout]
2 2 (3-3,1-4) arrange gtable[layout]
3 3 (4-4,1-4) arrange gtable[layout]
4 4 (5-5,2-3) arrange gtable[layout]
5 5 (1-1,1-4) arrange text[GRID.text.665]Code
# Save the plot if needed
ggsave("bivariate_priority_maps_all_8bins.png", combined_plot, width = 15, height = 16, dpi = 300)Budget: 0.1
Code
# Read all RDS files
edge_continental <- readRDS(here::here("Data/EDGE2_full_results_continental_averaged_budget0.1_replicates10.rds"))
edge_highseas <- readRDS(here::here("Data/EDGE2_full_highseas_results_averaged_budget0.1_replicates10.rds"))
fuse_continental <- readRDS(here::here("Data/FUSE_full_results_continental_averaged_budget0.1_replicates10.rds"))
fuse_highseas <- readRDS(here::here("Data/FUSE_full_highseas_results_averaged_budget0.1_replicates10.rds"))
# Get world map data and set projection
world <- ne_countries(scale = "medium", returnclass = "sf")
mcbryde_thomas_2 <- "+proj=mbt_s"
# Function to process and combine data
process_data <- function(edge_data, fuse_data) {
combined_data <- edge_data %>%
rename(EDGE_Priority = Priority) %>%
left_join(fuse_data %>% rename(FUSE_Priority = Priority),
by = c("Longitude", "Latitude"))
# Normalize priorities to 0-1 range
combined_data <- combined_data %>%
mutate(
EDGE_Priority_Norm = (EDGE_Priority - min(EDGE_Priority)) / (max(EDGE_Priority) - min(EDGE_Priority)),
FUSE_Priority_Norm = (FUSE_Priority - min(FUSE_Priority)) / (max(FUSE_Priority) - min(FUSE_Priority))
)
# Transform to sf object
data_sf <- st_as_sf(combined_data, coords = c("Longitude", "Latitude"), crs = 4326) %>%
st_transform(crs = mcbryde_thomas_2)
return(data_sf)
}
# Process continental and high seas data
continental_sf <- process_data(edge_continental, fuse_continental)
highseas_sf <- process_data(edge_highseas, fuse_highseas)
# Combine continental and high seas data for the combined map
combined_sf <- rbind(continental_sf, highseas_sf)
# Project the world map
world_projected <- st_transform(world, crs = mcbryde_thomas_2)
# Create color palette
map_pal_raw <- bi_pal(pal = 'PurpleOr', dim = 4, preview = FALSE)
map_pal_mtx <- matrix(map_pal_raw, nrow = 4, ncol = 4)
map_pal_mtx[3, ] <- colorspace::lighten(map_pal_mtx[3, ], .1)
map_pal_mtx[2, ] <- colorspace::lighten(map_pal_mtx[2, ], .2)
map_pal_mtx[1, ] <- colorspace::lighten(map_pal_mtx[1, ], .3)
map_pal_mtx[ , 3] <- colorspace::lighten(map_pal_mtx[ , 3], .1)
map_pal_mtx[ , 2] <- colorspace::lighten(map_pal_mtx[ , 2], .2)
map_pal_mtx[ , 1] <- colorspace::lighten(map_pal_mtx[ , 1], .3)
map_pal_mtx[1, 1] <- '#ffffee'
map_pal <- as.vector(map_pal_mtx) %>% setNames(names(map_pal_raw))
# Color mapping function
get_color <- function(edge, fuse) {
edge_class <- cut(edge, breaks = c(-Inf, 0.25, 0.5, 0.75, Inf), labels = 1:4)
fuse_class <- cut(fuse, breaks = c(-Inf, 0.25, 0.5, 0.75, Inf), labels = 1:4)
return(map_pal[(as.numeric(fuse_class)-1)*4 + as.numeric(edge_class)])
}
# Apply colors to all datasets
continental_sf$new_color <- mapply(get_color, continental_sf$EDGE_Priority_Norm, continental_sf$FUSE_Priority_Norm)
highseas_sf$new_color <- mapply(get_color, highseas_sf$EDGE_Priority_Norm, highseas_sf$FUSE_Priority_Norm)
combined_sf$new_color <- mapply(get_color, combined_sf$EDGE_Priority_Norm, combined_sf$FUSE_Priority_Norm)
# Create plot function
create_bivariate_plot <- function(data_sf, title) {
ggplot() +
geom_sf(data = data_sf, aes(color = new_color), size = 0.1, alpha = 1) +
geom_sf(data = world_projected, fill = "lightgray", color = "lightgray") +
geom_sf(data = globe_border, fill = NA, color = "grey70", size = 0.5) +
scale_color_identity() +
coord_sf() +
theme_minimal() +
labs(title = title,
x = NULL, y = NULL) +
theme(plot.title = element_text(hjust = 0.5))
}
# Create legend
legend_plot <- bi_legend(pal = map_pal, dim = 4,
xlab = 'EDGE2',
ylab = 'FUSE')
# Create the individual plots
continental_bivariate <- create_bivariate_plot(continental_sf, "Continental Waters: EDGE2 vs FUSE Priorities")
highseas_bivariate <- create_bivariate_plot(highseas_sf, "High Seas: EDGE2 vs FUSE Priorities")
combined_bivariate_01 <- create_bivariate_plot(combined_sf, "Combined Waters: EDGE2 vs FUSE Priorities; Budget: 0.1")
#Map for the manuscript
# Modify the create_bivariate_plot function for this specific case
combined_bivariate_01_ms <- ggplot() +
geom_sf(data = combined_sf, aes(color = new_color), size = 0.1, alpha = 1) +
geom_sf(data = world_projected, fill = "lightgray", color = "lightgray") +
geom_sf(data = globe_border, fill = NA, color = "grey70", size = 0.5) +
scale_color_identity() +
coord_sf() +
theme_minimal() +
labs(x = NULL, y = NULL) +
annotate("text", x = -Inf, y = Inf, label = "(A)",
hjust = -1, vjust = 2, size = 6, fontface = "bold") +
theme(panel.grid = element_blank(),
plot.margin = margin(t = 10, r = 10, b = 10, l = 10, unit = "pt"))
# Arrange plots with shared legend
layout <- rbind(
c(1, 1, 1),
c(2, 2, 2),
c(3, 3, 3),
c(4, 4, 4)
)
# Create legend with larger text
legend_plot <- bi_legend(pal = map_pal, dim = 4,
xlab = 'EDGE2',
ylab = 'FUSE',
size = 2) + # Base size for the legend elements
theme(
axis.title = element_text(size = 18, face = "bold"), # Larger axis titles
axis.text = element_blank(), # Larger axis text
legend.text = element_text(size = 12), # Larger legend text
legend.title = element_text(size = 14, face = "bold") # Larger legend title
)
# Keep the rest of your grid.arrange code the same
combined_plot <- grid.arrange(
continental_bivariate,
highseas_bivariate,
combined_bivariate_01,
legend_plot,
layout_matrix = layout,
heights = c(0.32, 0.32, 0.32, 0.15),
widths = unit(c(15, 15, 15), "inches"),
top = textGrob("Bivariate Maps of EDGE2 and FUSE Priorities",
gp = gpar(fontsize = 16, font = 2))
)
Code
# Display the combined plot
#print(combined_plot)
# Save the plot if needed
# ggsave("bivariate_priority_maps_all.png", combined_plot, width = 15, height = 16, dpi = 300)Manuscript maps
Code
layout <- rbind(
c(1, 1, 1),
c(2, 2, 2),
c(3, 3, 3)
)
combined_plot <- grid.arrange(
combined_bivariate_01_ms,
combined_bivariate_03_ms,
legend_plot,
layout_matrix = layout,
heights = c(0.32, 0.32, 0.15),
widths = unit(c(15, 15, 15), "inches") #,
# top = textGrob("Bivariate Maps of EDGE2 and FUSE Priorities",
# gp = gpar(fontsize = 16, font = 2))
)
Code
# TIFF version
ggsave(here::here("bivariate_maps_comparison_ms.png"),
combined_plot,
width = 10,
height = 12,
dpi = 300,
bg = "white")
#Suplementary figure:
layout <- rbind(
c(1, 2),
c(3, 4)
)
combined_plot <- grid.arrange(
combined_plot_FUSE_01,
combined_plot_FUSE_03,
combined_plot_EDGE2_01,
combined_plot_EDGE2_03,
layout_matrix = layout,
top = textGrob("Global Conservation Priorities",
gp = gpar(fontsize = 16, font = 2))
)
Code
# Save if needed
ggsave(here::here("priority_maps_grid.png"),
combined_plot,
width = 15,
height = 12,
dpi = 300,
bg = "white")FUSE : species level priorities
Budget: 0.3
Code
# Protection fraction summary
# Read the data
prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_03_continental.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
sp_in_data <- read_csv(here("Data", "continental_puvsp_harmonised.csv"))
# Extract all species names and FUSE values from sp
all_species <- sp$FUSE$info$Species
all_FUSE <- sp$FUSE$info$FUSE
# Get unique species in your data
Species_in_data <- sort(unique(sp_in_data$species_name))
# Create a mapping between species and their FUSE values
species_FUSE_map <- data.frame(
Species = all_species,
FUSE = as.numeric(all_FUSE)
)
# Filter the mapping to only include species in your data
filtered_species_FUSE <- species_FUSE_map[species_FUSE_map$Species %in% Species_in_data, ]
# Add Species and FUSE to prot_frac
prot_frac$Species <- filtered_species_FUSE$Species
prot_frac$FUSE <- filtered_species_FUSE$FUSE
# Create histogram for Mean_Protect_Fraction
hist_protect <- ggplot(prot_frac, aes(x = Mean_Protect_Fraction)) +
geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
scale_x_continuous(limits=c(0,1)) +
theme_minimal() +
labs(title = "Histogram of Mean Protect Fraction",
x = "Mean Protect Fraction",
y = "Count")
# Create histogram for EDGE2
hist_fuse <- ggplot(prot_frac, aes(x = FUSE)) +
geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
theme_minimal() +
labs(title = "Histogram of FUSE Scores",
x = "FUSE Score",
y = "Count")
# Create scatterplot
scatter_plot <- ggplot(prot_frac, aes(x = FUSE, y = Mean_Protect_Fraction)) +
geom_point(alpha = 0.6, color = "darkblue") +
theme_minimal() +
scale_y_continuous(limits=c(0,1)) +
labs(title = "Scatterplot: FUSE vs Mean Protect Fraction",
x = "FUSE Score",
y = "Mean Protect Fraction")
# Arrange plots in a grid
grid_plot <- grid.arrange(
hist_protect, hist_fuse, scatter_plot,
layout_matrix = rbind(c(1,2), c(3,3)),
widths = c(1, 1),
heights = c(1, 1)
)
Code
#High seas waters
# Protection fraction summary for high seas
# Read the data
prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_03_highseas.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
sp_in_data <- read_csv(here("Data", "highseas_puvsp_harmonised.csv"))
# Extract all species names and FUSE values from sp
all_species <- sp$FUSE$info$Species
all_FUSE <- sp$FUSE$info$FUSE
# Get unique species in your data
Species_in_data <- sort(unique(sp_in_data$species_name))
# Create a mapping between species and their FUSE values
species_FUSE_map <- data.frame(
Species = all_species,
FUSE = as.numeric(all_FUSE)
)
# Filter the mapping to only include species in your data
filtered_species_FUSE <- species_FUSE_map[species_FUSE_map$Species %in% Species_in_data, ]
# Add Species and FUSE to prot_frac
prot_frac$Species <- filtered_species_FUSE$Species
prot_frac$FUSE <- filtered_species_FUSE$FUSE
# Create histogram for Mean_Protect_Fraction
hist_protect <- ggplot(prot_frac, aes(x = Mean_Protect_Fraction)) +
geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
scale_x_continuous(limits=c(0,1)) +
theme_minimal() +
labs(title = "Histogram of Mean Protect Fraction (High Seas)",
x = "Mean Protect Fraction",
y = "Count")
# Create histogram for FUSE
hist_fuse <- ggplot(prot_frac, aes(x = FUSE)) +
geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
theme_minimal() +
labs(title = "Histogram of FUSE Scores (High Seas)",
x = "FUSE Score",
y = "Count")
# Create scatterplot
scatter_plot <- ggplot(prot_frac, aes(x = FUSE, y = Mean_Protect_Fraction)) +
geom_point(alpha = 0.6, color = "darkblue") +
theme_minimal() +
scale_y_continuous(limits=c(0,1)) +
labs(title = "Scatterplot: FUSE vs Mean Protect Fraction (High Seas)",
x = "FUSE Score",
y = "Mean Protect Fraction")
# Arrange plots in a grid
grid_plot <- grid.arrange(
hist_protect, hist_fuse, scatter_plot,
layout_matrix = rbind(c(1,2), c(3,3)),
widths = c(1, 1),
heights = c(1, 1)
)
Code
#Now combine both and weigth by range size
library(tidyverse)
library(gridExtra)
library(jsonlite)
library(here)
# Load all required data
continental_prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_03_continental.rds"))
highseas_prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_03_highseas.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
continental_sp_data <- read_csv(here("Data", "continental_puvsp_harmonised.csv"))
highseas_sp_data <- read_csv(here("Data", "highseas_puvsp_harmonised.csv"))
# Calculate continental range sizes
continental_ranges <- continental_sp_data %>%
group_by(species_name) %>%
summarise(continental_range = n())
# Calculate high seas range sizes
highseas_ranges <- highseas_sp_data %>%
group_by(species_name) %>%
summarise(highseas_range = n())
# Get species lists
continental_species <- sort(unique(continental_sp_data$species_name))
highseas_species <- sort(unique(highseas_sp_data$species_name))
# Create species-FUSE mapping
all_species <- sp$FUSE$info$Species
all_FUSE <- sp$FUSE$info$FUSE
species_FUSE_map <- data.frame(
Species = all_species,
FUSE = as.numeric(all_FUSE)
)
# Add species names to continental data
filtered_continental_FUSE <- species_FUSE_map[species_FUSE_map$Species %in% continental_species, ]
continental_prot_frac$Species <- filtered_continental_FUSE$Species
# Add species names to highseas data
filtered_highseas_FUSE <- species_FUSE_map[species_FUSE_map$Species %in% highseas_species, ]
highseas_prot_frac$Species <- filtered_highseas_FUSE$Species
# Combine the protection fractions with range sizes
combined_protection_FUSE_03 <- full_join(
continental_prot_frac %>%
select(Species, Mean_Protect_Fraction) %>%
rename(continental_protection = Mean_Protect_Fraction),
highseas_prot_frac %>%
select(Species, Mean_Protect_Fraction) %>%
rename(highseas_protection = Mean_Protect_Fraction),
by = "Species"
) %>%
# Join with the range sizes
left_join(continental_ranges, by = c("Species" = "species_name")) %>%
left_join(highseas_ranges, by = c("Species" = "species_name"))
# Calculate weighted protection
combined_protection_FUSE_03 <- combined_protection_FUSE_03 %>%
mutate(
# Replace NA with 0 for protection values and ranges
continental_protection = replace_na(continental_protection, 0),
highseas_protection = replace_na(highseas_protection, 0),
continental_range = replace_na(continental_range, 0),
highseas_range = replace_na(highseas_range, 0),
# Calculate total range
total_range = continental_range + highseas_range,
# Calculate weighted protection
weighted_protection = (continental_protection * continental_range +
highseas_protection * highseas_range) /
total_range
)
# Add FUSE scores
combined_protection_FUSE_03 <- left_join(combined_protection_FUSE_03, species_FUSE_map, by = "Species")
# Create summary statistics
summary_stats <- combined_protection_FUSE_03 %>%
select(-Species) %>%
summarise(across(everything(), list(
min = ~min(., na.rm = TRUE),
q1 = ~quantile(., 0.25, na.rm = TRUE),
median = ~median(., na.rm = TRUE),
mean = ~mean(., na.rm = TRUE),
q3 = ~quantile(., 0.75, na.rm = TRUE),
max = ~max(., na.rm = TRUE)
))) %>%
pivot_longer(everything(),
names_to = c("variable", "stat"),
names_pattern = "(.*)_(.*)") %>%
pivot_wider(names_from = stat, values_from = value)
# Create and format the flextable
library(flextable)
summary_table <- flextable(summary_stats) %>%
set_header_labels(
variable = "Variable",
min = "Minimum",
q1 = "1st Quartile",
median = "Median",
mean = "Mean",
q3 = "3rd Quartile",
max = "Maximum"
) %>%
colformat_double(digits = 3) %>%
theme_vanilla() %>%
autofit()
# Display the table
summary_tableVariable | Minimum | 1st Quartile | Median | Mean | 3rd Quartile | Maximum |
|---|---|---|---|---|---|---|
continental_protection | 0.000 | 0.302 | 0.308 | 0.352 | 0.336 | 1.000 |
highseas_protection | 0.000 | 0.000 | 0.000 | 0.101 | 0.000 | 1.000 |
continental_range | 0.000 | 56.000 | 193.000 | 1,248.928 | 650.000 | 40,875.000 |
highseas_range | 0.000 | 0.000 | 0.000 | 805.935 | 0.000 | 63,442.000 |
total_range | 1.000 | 56.000 | 200.000 | 2,054.864 | 655.000 | 104,317.000 |
weighted_protection | 0.300 | 0.303 | 0.309 | 0.355 | 0.341 | 1.000 |
FUSE | 0.000 | 0.000 | 0.001 | 0.059 | 0.031 | 1.000 |
Code
# Create visualizations
hist_protect <- ggplot(combined_protection_FUSE_03, aes(x = weighted_protection)) +
geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
scale_x_continuous(limits=c(0,1)) +
theme_minimal() +
labs(title = "Histogram of Range-Weighted Protection Fraction",
x = "Weighted Protection Fraction",
y = "Count")
hist_fuse <- ggplot(combined_protection_FUSE_03, aes(x = FUSE)) +
geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
theme_minimal() +
labs(title = "Histogram of FUSE Scores",
x = "FUSE Score",
y = "Count")
scatter_plot <- ggplot(combined_protection_FUSE_03, aes(x = FUSE, y = weighted_protection)) +
geom_point(alpha = 0.6, color = "darkblue") +
theme_minimal() +
scale_y_continuous(limits=c(0,1)) +
labs(title = "Scatterplot: FUSE vs Weighted Protection Fraction",
x = "FUSE Score",
y = "Weighted Protection Fraction")
# Create species range type summary
range_type_summary <- combined_protection_FUSE_03 %>%
summarise(
total_species = n(),
continental_only = sum(highseas_range == 0 & continental_range > 0),
highseas_only = sum(continental_range == 0 & highseas_range > 0),
both_ranges = sum(continental_range > 0 & highseas_range > 0)
) %>%
pivot_longer(everything(),
names_to = "Distribution Type",
values_to = "Number of Species")
# Create and format the flextable
range_type_table <- flextable(range_type_summary) %>%
set_header_labels(
`Distribution Type` = "Distribution Type",
`Number of Species` = "Number of Species"
) %>%
theme_vanilla() %>%
autofit()
# Display the table
range_type_tableDistribution Type | Number of Species |
|---|---|
total_species | 1,005 |
continental_only | 802 |
highseas_only | 5 |
both_ranges | 198 |
Code
# Arrange plots in a grid
grid_plot <- grid.arrange(
hist_protect, hist_fuse, scatter_plot,
layout_matrix = rbind(c(1,2), c(3,3)),
widths = c(1, 1),
heights = c(1, 1)
)
Code
# Save the combined protection data
saveRDS(combined_protection_FUSE_03, file = here::here("Data", "combined_protection_FUSE_03.rds"))Budget: 0.1
Code
# Protection fraction summary
# Read the data
prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_01_continental.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
sp_in_data <- read_csv(here("Data", "continental_puvsp_harmonised.csv"))
# Extract all species names and FUSE values from sp
all_species <- sp$FUSE$info$Species
all_FUSE <- sp$FUSE$info$FUSE
# Get unique species in your data
Species_in_data <- sort(unique(sp_in_data$species_name))
# Create a mapping between species and their FUSE values
species_FUSE_map <- data.frame(
Species = all_species,
FUSE = as.numeric(all_FUSE)
)
# Filter the mapping to only include species in your data
filtered_species_FUSE <- species_FUSE_map[species_FUSE_map$Species %in% Species_in_data, ]
# Add Species and FUSE to prot_frac
prot_frac$Species <- filtered_species_FUSE$Species
prot_frac$FUSE <- filtered_species_FUSE$FUSE
# Create histogram for Mean_Protect_Fraction
hist_protect <- ggplot(prot_frac, aes(x = Mean_Protect_Fraction)) +
geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
scale_x_continuous(limits=c(0,1)) +
theme_minimal() +
labs(title = "Histogram of Mean Protect Fraction\n(Continental)",
x = "Mean Protect Fraction",
y = "Count")
# Create histogram for FUSE
hist_fuse <- ggplot(prot_frac, aes(x = FUSE)) +
geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
theme_minimal() +
labs(title = "Histogram of FUSE Scores\n(Continental)",
x = "FUSE Score",
y = "Count")
# Create scatterplot
scatter_plot <- ggplot(prot_frac, aes(x = FUSE, y = Mean_Protect_Fraction)) +
geom_point(alpha = 0.6, color = "darkblue") +
theme_minimal() +
scale_y_continuous(limits=c(0,1)) +
labs(title = "Scatterplot: FUSE vs Mean Protect Fraction (Continental)",
x = "FUSE Score",
y = "Mean Protect Fraction")
# Arrange plots in a grid
grid_plot <- grid.arrange(
hist_protect, hist_fuse, scatter_plot,
layout_matrix = rbind(c(1,2), c(3,3)),
widths = c(1, 1),
heights = c(1, 1)
)
Code
#High seas waters
# Protection fraction summary for high seas
# Read the data
prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_01_highseas.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
sp_in_data <- read_csv(here("Data", "highseas_puvsp_harmonised.csv"))
# Extract all species names and FUSE values from sp
all_species <- sp$FUSE$info$Species
all_FUSE <- sp$FUSE$info$FUSE
# Get unique species in your data
Species_in_data <- sort(unique(sp_in_data$species_name))
# Create a mapping between species and their FUSE values
species_FUSE_map <- data.frame(
Species = all_species,
FUSE = as.numeric(all_FUSE)
)
# Filter the mapping to only include species in your data
filtered_species_FUSE <- species_FUSE_map[species_FUSE_map$Species %in% Species_in_data, ]
# Add Species and FUSE to prot_frac
prot_frac$Species <- filtered_species_FUSE$Species
prot_frac$FUSE <- filtered_species_FUSE$FUSE
# Create histogram for Mean_Protect_Fraction
hist_protect <- ggplot(prot_frac, aes(x = Mean_Protect_Fraction)) +
geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
scale_x_continuous(limits=c(0,1)) +
theme_minimal() +
labs(title = "Histogram of Mean Protect Fraction\n(High Seas)",
x = "Mean Protect Fraction",
y = "Count")
# Create histogram for FUSE
hist_fuse <- ggplot(prot_frac, aes(x = FUSE)) +
geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
theme_minimal() +
labs(title = "Histogram of FUSE Scores\n(High Seas)",
x = "FUSE Score",
y = "Count")
# Create scatterplot
scatter_plot <- ggplot(prot_frac, aes(x = FUSE, y = Mean_Protect_Fraction)) +
geom_point(alpha = 0.6, color = "darkblue") +
theme_minimal() +
scale_y_continuous(limits=c(0,1)) +
labs(title = "Scatterplot: FUSE vs Mean Protect Fraction (High Seas)",
x = "FUSE Score",
y = "Mean Protect Fraction")
# Arrange plots in a grid
grid_plot <- grid.arrange(
hist_protect, hist_fuse, scatter_plot,
layout_matrix = rbind(c(1,2), c(3,3)),
widths = c(1, 1),
heights = c(1, 1)
)
Code
#Now combine both and weigth by range size
library(tidyverse)
library(gridExtra)
library(jsonlite)
library(here)
# Load all required data
continental_prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_01_continental.rds"))
highseas_prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_01_highseas.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
continental_sp_data <- read_csv(here("Data", "continental_puvsp_harmonised.csv"))
highseas_sp_data <- read_csv(here("Data", "highseas_puvsp_harmonised.csv"))
# Calculate continental range sizes
continental_ranges <- continental_sp_data %>%
group_by(species_name) %>%
summarise(continental_range = n())
# Calculate high seas range sizes
highseas_ranges <- highseas_sp_data %>%
group_by(species_name) %>%
summarise(highseas_range = n())
# Get species lists
continental_species <- sort(unique(continental_sp_data$species_name))
highseas_species <- sort(unique(highseas_sp_data$species_name))
# Create species-FUSE mapping
all_species <- sp$FUSE$info$Species
all_FUSE <- sp$FUSE$info$FUSE
species_FUSE_map <- data.frame(
Species = all_species,
FUSE = as.numeric(all_FUSE)
)
# Add species names to continental data
filtered_continental_FUSE <- species_FUSE_map[species_FUSE_map$Species %in% continental_species, ]
continental_prot_frac$Species <- filtered_continental_FUSE$Species
# Add species names to highseas data
filtered_highseas_FUSE <- species_FUSE_map[species_FUSE_map$Species %in% highseas_species, ]
highseas_prot_frac$Species <- filtered_highseas_FUSE$Species
# Combine the protection fractions with range sizes
combined_protection_FUSE_01 <- full_join(
continental_prot_frac %>%
select(Species, Mean_Protect_Fraction) %>%
rename(continental_protection = Mean_Protect_Fraction),
highseas_prot_frac %>%
select(Species, Mean_Protect_Fraction) %>%
rename(highseas_protection = Mean_Protect_Fraction),
by = "Species"
) %>%
# Join with the range sizes
left_join(continental_ranges, by = c("Species" = "species_name")) %>%
left_join(highseas_ranges, by = c("Species" = "species_name"))
# Calculate weighted protection
combined_protection_FUSE_01 <- combined_protection_FUSE_01 %>%
mutate(
# Replace NA with 0 for protection values and ranges
continental_protection = replace_na(continental_protection, 0),
highseas_protection = replace_na(highseas_protection, 0),
continental_range = replace_na(continental_range, 0),
highseas_range = replace_na(highseas_range, 0),
# Calculate total range
total_range = continental_range + highseas_range,
# Calculate weighted protection
weighted_protection = (continental_protection * continental_range +
highseas_protection * highseas_range) /
total_range
)
# Add FUSE scores
combined_protection_FUSE_01 <- left_join(combined_protection_FUSE_01, species_FUSE_map, by = "Species")
# Create summary statistics
summary_stats <- combined_protection_FUSE_01 %>%
select(-Species) %>%
summarise(across(everything(), list(
min = ~min(., na.rm = TRUE),
q1 = ~quantile(., 0.25, na.rm = TRUE),
median = ~median(., na.rm = TRUE),
mean = ~mean(., na.rm = TRUE),
q3 = ~quantile(., 0.75, na.rm = TRUE),
max = ~max(., na.rm = TRUE)
))) %>%
pivot_longer(everything(),
names_to = c("variable", "stat"),
names_pattern = "(.*)_(.*)") %>%
pivot_wider(names_from = stat, values_from = value)
# Create and format the flextable
library(flextable)
summary_table <- flextable(summary_stats) %>%
set_header_labels(
variable = "Variable",
min = "Minimum",
q1 = "1st Quartile",
median = "Median",
mean = "Mean",
q3 = "3rd Quartile",
max = "Maximum"
) %>%
colformat_double(digits = 3) %>%
theme_vanilla() %>%
autofit()
# Display the table
summary_tableVariable | Minimum | 1st Quartile | Median | Mean | 3rd Quartile | Maximum |
|---|---|---|---|---|---|---|
continental_protection | 0.000 | 0.103 | 0.110 | 0.187 | 0.194 | 1.000 |
highseas_protection | 0.000 | 0.000 | 0.000 | 0.075 | 0.000 | 1.000 |
continental_range | 0.000 | 56.000 | 193.000 | 1,248.928 | 650.000 | 40,875.000 |
highseas_range | 0.000 | 0.000 | 0.000 | 805.935 | 0.000 | 63,442.000 |
total_range | 1.000 | 56.000 | 200.000 | 2,054.864 | 655.000 | 104,317.000 |
weighted_protection | 0.100 | 0.103 | 0.112 | 0.190 | 0.200 | 1.000 |
FUSE | 0.000 | 0.000 | 0.001 | 0.059 | 0.031 | 1.000 |
Code
# Create visualizations
hist_protect <- ggplot(combined_protection_FUSE_01, aes(x = weighted_protection)) +
geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
scale_x_continuous(limits=c(0,1)) +
theme_minimal() +
labs(title = "Histogram of Range-Weighted Protection Fraction",
x = "Weighted Protection Fraction",
y = "Count")
hist_fuse <- ggplot(combined_protection_FUSE_01, aes(x = FUSE)) +
geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
theme_minimal() +
labs(title = "Histogram of FUSE Scores",
x = "FUSE Score",
y = "Count")
scatter_plot <- ggplot(combined_protection_FUSE_01, aes(x = FUSE, y = weighted_protection)) +
geom_point(alpha = 0.6, color = "darkblue") +
theme_minimal() +
scale_y_continuous(limits=c(0,1)) +
labs(title = "Scatterplot: FUSE vs Weighted Protection Fraction",
x = "FUSE Score",
y = "Weighted Protection Fraction")
# Create species range type summary
range_type_summary <- combined_protection_FUSE_01 %>%
summarise(
total_species = n(),
continental_only = sum(highseas_range == 0 & continental_range > 0),
highseas_only = sum(continental_range == 0 & highseas_range > 0),
both_ranges = sum(continental_range > 0 & highseas_range > 0)
) %>%
pivot_longer(everything(),
names_to = "Distribution Type",
values_to = "Number of Species")
# Create and format the flextable
range_type_table <- flextable(range_type_summary) %>%
set_header_labels(
`Distribution Type` = "Distribution Type",
`Number of Species` = "Number of Species"
) %>%
theme_vanilla() %>%
autofit()
# Display the table
range_type_tableDistribution Type | Number of Species |
|---|---|
total_species | 1,005 |
continental_only | 802 |
highseas_only | 5 |
both_ranges | 198 |
Code
# Arrange plots in a grid
grid_plot <- grid.arrange(
hist_protect, hist_fuse, scatter_plot,
layout_matrix = rbind(c(1,2), c(3,3)),
widths = c(1, 1),
heights = c(1, 1)
)
Code
# Save the combined protection data
saveRDS(combined_protection_FUSE_01, file = here::here("Data", "combined_protection_FUSE_01.rds"))EDGE2 : species level priorities
Budget: 0.3
Code
# Protection fraction summary
# Read the data
prot_frac <- readRDS(here::here("Data/protect_fraction_summary_EDGE2_03_continental.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
sp_in_data <- read_csv(here("Data", "continental_puvsp_harmonised.csv"))
# Extract all species names and EDGE2 values from sp
all_species <- sp$EDGE2$info$Species
all_EDGE2 <- sp$EDGE2$info$EDGE2
# Get unique species in your data
Species_in_data <- sort(unique(sp_in_data$species_name))
# Create a mapping between species and their EDGE2 values
species_EDGE2_map <- data.frame(
Species = all_species,
EDGE2 = as.numeric(all_EDGE2)
)
# Filter the mapping to only include species in your data
filtered_species_EDGE2 <- species_EDGE2_map[species_EDGE2_map$Species %in% Species_in_data, ]
# Add Species and EDGE2 to prot_frac
prot_frac$Species <- filtered_species_EDGE2$Species
prot_frac$EDGE2 <- filtered_species_EDGE2$EDGE2
# Create histogram for Mean_Protect_Fraction
hist_protect <- ggplot(prot_frac, aes(x = Mean_Protect_Fraction)) +
geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
scale_x_continuous(limits=c(0,1)) +
theme_minimal() +
labs(title = "Histogram of Mean Protect Fraction\n(Continental)",
x = "Mean Protect Fraction",
y = "Count")
# Create histogram for EDGE2
hist_EDGE2 <- ggplot(prot_frac, aes(x = EDGE2)) +
geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
theme_minimal() +
labs(title = "Histogram of EDGE2 Scores\n(Continental)",
x = "EDGE2 Score",
y = "Count")
# Create scatterplot
scatter_plot <- ggplot(prot_frac, aes(x = EDGE2, y = Mean_Protect_Fraction)) +
geom_point(alpha = 0.6, color = "darkblue") +
theme_minimal() +
scale_y_continuous(limits=c(0,1)) +
labs(title = "Scatterplot: EDGE2 vs Mean Protect Fraction (Continental)",
x = "EDGE2 Score",
y = "Mean Protect Fraction")
# Arrange plots in a grid
grid_plot <- grid.arrange(
hist_protect, hist_EDGE2, scatter_plot,
layout_matrix = rbind(c(1,2), c(3,3)),
widths = c(1, 1),
heights = c(1, 1)
)
Code
#High seas waters
# Protection fraction summary for high seas
# Read the data
prot_frac <- readRDS(here::here("Data/protect_fraction_summary_EDGE2_03_highseas.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
sp_in_data <- read_csv(here("Data", "highseas_puvsp_harmonised.csv"))
# Extract all species names and EDGE2 values from sp
all_species <- sp$EDGE2$info$Species
all_EDGE2 <- sp$EDGE2$info$EDGE2
# Get unique species in your data
Species_in_data <- sort(unique(sp_in_data$species_name))
# Create a mapping between species and their EDGE2 values
species_EDGE2_map <- data.frame(
Species = all_species,
EDGE2 = as.numeric(all_EDGE2)
)
# Filter the mapping to only include species in your data
filtered_species_EDGE2 <- species_EDGE2_map[species_EDGE2_map$Species %in% Species_in_data, ]
# Add Species and EDGE2 to prot_frac
prot_frac$Species <- filtered_species_EDGE2$Species
prot_frac$EDGE2 <- filtered_species_EDGE2$EDGE2
# Create histogram for Mean_Protect_Fraction
hist_protect <- ggplot(prot_frac, aes(x = Mean_Protect_Fraction)) +
geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
scale_x_continuous(limits=c(0,1)) +
theme_minimal() +
labs(title = "Histogram of Mean Protect Fraction\n(High Seas)",
x = "Mean Protect Fraction",
y = "Count")
# Create histogram for EDGE2
hist_EDGE2 <- ggplot(prot_frac, aes(x = EDGE2)) +
geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
theme_minimal() +
labs(title = "Histogram of EDGE2 Scores\n(High Seas)",
x = "EDGE2 Score",
y = "Count")
# Create scatterplot
scatter_plot <- ggplot(prot_frac, aes(x = EDGE2, y = Mean_Protect_Fraction)) +
geom_point(alpha = 0.6, color = "darkblue") +
theme_minimal() +
scale_y_continuous(limits=c(0,1)) +
labs(title = "Scatterplot: EDGE2 vs Mean Protect Fraction (High Seas)",
x = "EDGE2 Score",
y = "Mean Protect Fraction")
# Arrange plots in a grid
grid_plot <- grid.arrange(
hist_protect, hist_EDGE2, scatter_plot,
layout_matrix = rbind(c(1,2), c(3,3)),
widths = c(1, 1),
heights = c(1, 1)
)
Code
#Now combine both and weigth by range size
library(tidyverse)
library(gridExtra)
library(jsonlite)
library(here)
# Load all required data
continental_prot_frac <- readRDS(here::here("Data/protect_fraction_summary_EDGE2_03_continental.rds"))
highseas_prot_frac <- readRDS(here::here("Data/protect_fraction_summary_EDGE2_03_highseas.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
continental_sp_data <- read_csv(here("Data", "continental_puvsp_harmonised.csv"))
highseas_sp_data <- read_csv(here("Data", "highseas_puvsp_harmonised.csv"))
# Calculate continental range sizes
continental_ranges <- continental_sp_data %>%
group_by(species_name) %>%
summarise(continental_range = n())
# Calculate high seas range sizes
highseas_ranges <- highseas_sp_data %>%
group_by(species_name) %>%
summarise(highseas_range = n())
# Get species lists
continental_species <- sort(unique(continental_sp_data$species_name))
highseas_species <- sort(unique(highseas_sp_data$species_name))
# Create species-EDGE2 mapping
all_species <- sp$EDGE2$info$Species
all_EDGE2 <- sp$EDGE2$info$EDGE2
species_EDGE2_map <- data.frame(
Species = all_species,
EDGE2 = as.numeric(all_EDGE2)
)
# Add species names to continental data
filtered_continental_EDGE2 <- species_EDGE2_map[species_EDGE2_map$Species %in% continental_species, ]
continental_prot_frac$Species <- filtered_continental_EDGE2$Species
# Add species names to highseas data
filtered_highseas_EDGE2 <- species_EDGE2_map[species_EDGE2_map$Species %in% highseas_species, ]
highseas_prot_frac$Species <- filtered_highseas_EDGE2$Species
# Combine the protection fractions with range sizes
combined_protection_EDGE2_03 <- full_join(
continental_prot_frac %>%
select(Species, Mean_Protect_Fraction) %>%
rename(continental_protection = Mean_Protect_Fraction),
highseas_prot_frac %>%
select(Species, Mean_Protect_Fraction) %>%
rename(highseas_protection = Mean_Protect_Fraction),
by = "Species"
) %>%
# Join with the range sizes
left_join(continental_ranges, by = c("Species" = "species_name")) %>%
left_join(highseas_ranges, by = c("Species" = "species_name"))
# Calculate weighted protection
combined_protection_EDGE2_03 <- combined_protection_EDGE2_03 %>%
mutate(
# Replace NA with 0 for protection values and ranges
continental_protection = replace_na(continental_protection, 0),
highseas_protection = replace_na(highseas_protection, 0),
continental_range = replace_na(continental_range, 0),
highseas_range = replace_na(highseas_range, 0),
# Calculate total range
total_range = continental_range + highseas_range,
# Calculate weighted protection
weighted_protection = (continental_protection * continental_range +
highseas_protection * highseas_range) /
total_range
)
# Add EDGE2 scores
combined_protection_EDGE2_03 <- left_join(combined_protection_EDGE2_03, species_EDGE2_map, by = "Species")
# Create summary statistics
summary_stats <- combined_protection_EDGE2_03 %>%
select(-Species) %>%
summarise(across(everything(), list(
min = ~min(., na.rm = TRUE),
q1 = ~quantile(., 0.25, na.rm = TRUE),
median = ~median(., na.rm = TRUE),
mean = ~mean(., na.rm = TRUE),
q3 = ~quantile(., 0.75, na.rm = TRUE),
max = ~max(., na.rm = TRUE)
))) %>%
pivot_longer(everything(),
names_to = c("variable", "stat"),
names_pattern = "(.*)_(.*)") %>%
pivot_wider(names_from = stat, values_from = value)
# Create and format the flextable
library(flextable)
summary_table <- flextable(summary_stats) %>%
set_header_labels(
variable = "Variable",
min = "Minimum",
q1 = "1st Quartile",
median = "Median",
mean = "Mean",
q3 = "3rd Quartile",
max = "Maximum"
) %>%
colformat_double(digits = 3) %>%
theme_vanilla() %>%
autofit()
# Display the table
summary_tableVariable | Minimum | 1st Quartile | Median | Mean | 3rd Quartile | Maximum |
|---|---|---|---|---|---|---|
continental_protection | 0.000 | 0.302 | 0.307 | 0.348 | 0.330 | 1.000 |
highseas_protection | 0.000 | 0.000 | 0.000 | 0.096 | 0.000 | 1.000 |
continental_range | 0.000 | 56.000 | 193.000 | 1,248.928 | 650.000 | 40,875.000 |
highseas_range | 0.000 | 0.000 | 0.000 | 805.935 | 0.000 | 63,442.000 |
total_range | 1.000 | 56.000 | 200.000 | 2,054.864 | 655.000 | 104,317.000 |
weighted_protection | 0.300 | 0.303 | 0.308 | 0.351 | 0.333 | 1.000 |
EDGE2 | 0.000 | 0.000 | 0.001 | 0.045 | 0.018 | 1.000 |
Code
# Create visualizations
hist_protect <- ggplot(combined_protection_EDGE2_03, aes(x = weighted_protection)) +
geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
scale_x_continuous(limits=c(0,1)) +
theme_minimal() +
labs(title = "Histogram of Range-Weighted Protection Fraction",
x = "Weighted Protection Fraction",
y = "Count")
hist_EDGE2 <- ggplot(combined_protection_EDGE2_03, aes(x = EDGE2)) +
geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
theme_minimal() +
labs(title = "Histogram of EDGE2 Scores",
x = "EDGE2 Score",
y = "Count")
scatter_plot <- ggplot(combined_protection_EDGE2_03, aes(x = EDGE2, y = weighted_protection)) +
geom_point(alpha = 0.6, color = "darkblue") +
theme_minimal() +
scale_y_continuous(limits=c(0,1)) +
labs(title = "Scatterplot: EDGE2 vs Weighted Protection Fraction",
x = "EDGE2 Score",
y = "Weighted Protection Fraction")
# Create species range type summary
range_type_summary <- combined_protection_EDGE2_03 %>%
summarise(
total_species = n(),
continental_only = sum(highseas_range == 0 & continental_range > 0),
highseas_only = sum(continental_range == 0 & highseas_range > 0),
both_ranges = sum(continental_range > 0 & highseas_range > 0)
) %>%
pivot_longer(everything(),
names_to = "Distribution Type",
values_to = "Number of Species")
# Create and format the flextable
range_type_table <- flextable(range_type_summary) %>%
set_header_labels(
`Distribution Type` = "Distribution Type",
`Number of Species` = "Number of Species"
) %>%
theme_vanilla() %>%
autofit()
# Display the table
range_type_tableDistribution Type | Number of Species |
|---|---|
total_species | 1,005 |
continental_only | 802 |
highseas_only | 5 |
both_ranges | 198 |
Code
# Arrange plots in a grid
grid_plot <- grid.arrange(
hist_protect, hist_EDGE2, scatter_plot,
layout_matrix = rbind(c(1,2), c(3,3)),
widths = c(1, 1),
heights = c(1, 1)
)
Code
# Save the combined protection data
saveRDS(combined_protection_EDGE2_03, file = here::here("Data", "combined_protection_EDGE2_03.rds"))Budget: 0.1
Code
# Protection fraction summary
# Read the data
prot_frac <- readRDS(here::here("Data/protect_fraction_summary_EDGE2_01_continental.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
sp_in_data <- read_csv(here("Data", "continental_puvsp_harmonised.csv"))
# Extract all species names and EDGE2 values from sp
all_species <- sp$EDGE2$info$Species
all_EDGE2 <- sp$EDGE2$info$EDGE2
# Get unique species in your data
Species_in_data <- sort(unique(sp_in_data$species_name))
# Create a mapping between species and their EDGE2 values
species_EDGE2_map <- data.frame(
Species = all_species,
EDGE2 = as.numeric(all_EDGE2)
)
# Filter the mapping to only include species in your data
filtered_species_EDGE2 <- species_EDGE2_map[species_EDGE2_map$Species %in% Species_in_data, ]
# Add Species and EDGE2 to prot_frac
prot_frac$Species <- filtered_species_EDGE2$Species
prot_frac$EDGE2 <- filtered_species_EDGE2$EDGE2
# Create histogram for Mean_Protect_Fraction
hist_protect <- ggplot(prot_frac, aes(x = Mean_Protect_Fraction)) +
geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
scale_x_continuous(limits=c(0,1)) +
theme_minimal() +
labs(title = "Histogram of Mean Protect Fraction\n(Continental)",
x = "Mean Protect Fraction",
y = "Count")
# Create histogram for EDGE2
hist_EDGE2 <- ggplot(prot_frac, aes(x = EDGE2)) +
geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
theme_minimal() +
labs(title = "Histogram of EDGE2 Scores\n(Continental)",
x = "EDGE2 Score",
y = "Count")
# Create scatterplot
scatter_plot <- ggplot(prot_frac, aes(x = EDGE2, y = Mean_Protect_Fraction)) +
geom_point(alpha = 0.6, color = "darkblue") +
theme_minimal() +
scale_y_continuous(limits=c(0,1)) +
labs(title = "Scatterplot: EDGE2 vs Mean Protect Fraction (Continental)",
x = "EDGE2 Score",
y = "Mean Protect Fraction")
# Arrange plots in a grid
grid_plot <- grid.arrange(
hist_protect, hist_EDGE2, scatter_plot,
layout_matrix = rbind(c(1,2), c(3,3)),
widths = c(1, 1),
heights = c(1, 1)
)
Code
#High seas waters
# Protection fraction summary for high seas
# Read the data
prot_frac <- readRDS(here::here("Data/protect_fraction_summary_EDGE2_01_highseas.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
sp_in_data <- read_csv(here("Data", "highseas_puvsp_harmonised.csv"))
# Extract all species names and EDGE2 values from sp
all_species <- sp$EDGE2$info$Species
all_EDGE2 <- sp$EDGE2$info$EDGE2
# Get unique species in your data
Species_in_data <- sort(unique(sp_in_data$species_name))
# Create a mapping between species and their EDGE2 values
species_EDGE2_map <- data.frame(
Species = all_species,
EDGE2 = as.numeric(all_EDGE2)
)
# Filter the mapping to only include species in your data
filtered_species_EDGE2 <- species_EDGE2_map[species_EDGE2_map$Species %in% Species_in_data, ]
# Add Species and EDGE2 to prot_frac
prot_frac$Species <- filtered_species_EDGE2$Species
prot_frac$EDGE2 <- filtered_species_EDGE2$EDGE2
# Create histogram for Mean_Protect_Fraction
hist_protect <- ggplot(prot_frac, aes(x = Mean_Protect_Fraction)) +
geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
scale_x_continuous(limits=c(0,1)) +
theme_minimal() +
labs(title = "Histogram of Mean Protect Fraction\n(High Seas)",
x = "Mean Protect Fraction",
y = "Count")
# Create histogram for EDGE2
hist_EDGE2 <- ggplot(prot_frac, aes(x = EDGE2)) +
geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
theme_minimal() +
labs(title = "Histogram of EDGE2 Scores\n(High Seas)",
x = "EDGE2 Score",
y = "Count")
# Create scatterplot
scatter_plot <- ggplot(prot_frac, aes(x = EDGE2, y = Mean_Protect_Fraction)) +
geom_point(alpha = 0.6, color = "darkblue") +
theme_minimal() +
scale_y_continuous(limits=c(0,1)) +
labs(title = "Scatterplot: EDGE2 vs Mean Protect Fraction (High Seas)",
x = "EDGE2 Score",
y = "Mean Protect Fraction")
# Arrange plots in a grid
grid_plot <- grid.arrange(
hist_protect, hist_EDGE2, scatter_plot,
layout_matrix = rbind(c(1,2), c(3,3)),
widths = c(1, 1),
heights = c(1, 1)
)
Code
library(tidyverse)
library(gridExtra)
library(jsonlite)
library(here)
# Load all required data
continental_prot_frac <- readRDS(here::here("Data/protect_fraction_summary_EDGE2_01_continental.rds"))
highseas_prot_frac <- readRDS(here::here("Data/protect_fraction_summary_EDGE2_01_highseas.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
continental_sp_data <- read_csv(here("Data", "continental_puvsp_harmonised.csv"))
highseas_sp_data <- read_csv(here("Data", "highseas_puvsp_harmonised.csv"))
# Calculate continental range sizes
continental_ranges <- continental_sp_data %>%
group_by(species_name) %>%
summarise(continental_range = n())
# Calculate high seas range sizes
highseas_ranges <- highseas_sp_data %>%
group_by(species_name) %>%
summarise(highseas_range = n())
# Get species lists
continental_species <- sort(unique(continental_sp_data$species_name))
highseas_species <- sort(unique(highseas_sp_data$species_name))
# Create species-EDGE2 mapping
all_species <- sp$EDGE2$info$Species
all_EDGE2 <- sp$EDGE2$info$EDGE2
species_EDGE2_map <- data.frame(
Species = all_species,
EDGE2 = as.numeric(all_EDGE2)
)
# Add species names to continental data
filtered_continental_EDGE2 <- species_EDGE2_map[species_EDGE2_map$Species %in% continental_species, ]
continental_prot_frac$Species <- filtered_continental_EDGE2$Species
# Add species names to highseas data
filtered_highseas_EDGE2 <- species_EDGE2_map[species_EDGE2_map$Species %in% highseas_species, ]
highseas_prot_frac$Species <- filtered_highseas_EDGE2$Species
# Combine the protection fractions with range sizes
combined_protection_EDGE2_01 <- full_join(
continental_prot_frac %>%
select(Species, Mean_Protect_Fraction) %>%
rename(continental_protection = Mean_Protect_Fraction),
highseas_prot_frac %>%
select(Species, Mean_Protect_Fraction) %>%
rename(highseas_protection = Mean_Protect_Fraction),
by = "Species"
) %>%
# Join with the range sizes
left_join(continental_ranges, by = c("Species" = "species_name")) %>%
left_join(highseas_ranges, by = c("Species" = "species_name"))
# Calculate weighted protection
combined_protection_EDGE2_01 <- combined_protection_EDGE2_01 %>%
mutate(
# Replace NA with 0 for protection values and ranges
continental_protection = replace_na(continental_protection, 0),
highseas_protection = replace_na(highseas_protection, 0),
continental_range = replace_na(continental_range, 0),
highseas_range = replace_na(highseas_range, 0),
# Calculate total range
total_range = continental_range + highseas_range,
# Calculate weighted protection
weighted_protection = (continental_protection * continental_range +
highseas_protection * highseas_range) /
total_range
)
# Add EDGE2 scores
combined_protection_EDGE2_01 <- left_join(combined_protection_EDGE2_01, species_EDGE2_map, by = "Species")
# Create summary statistics
summary_stats <- combined_protection_EDGE2_01 %>%
select(-Species) %>%
summarise(across(everything(), list(
min = ~min(., na.rm = TRUE),
q1 = ~quantile(., 0.25, na.rm = TRUE),
median = ~median(., na.rm = TRUE),
mean = ~mean(., na.rm = TRUE),
q3 = ~quantile(., 0.75, na.rm = TRUE),
max = ~max(., na.rm = TRUE)
))) %>%
pivot_longer(everything(),
names_to = c("variable", "stat"),
names_pattern = "(.*)_(.*)") %>%
pivot_wider(names_from = stat, values_from = value)
# Create and format the flextable
library(flextable)
summary_table <- flextable(summary_stats) %>%
set_header_labels(
variable = "Variable",
min = "Minimum",
q1 = "1st Quartile",
median = "Median",
mean = "Mean",
q3 = "3rd Quartile",
max = "Maximum"
) %>%
colformat_double(digits = 3) %>%
theme_vanilla() %>%
autofit()
# Display the table
summary_tableVariable | Minimum | 1st Quartile | Median | Mean | 3rd Quartile | Maximum |
|---|---|---|---|---|---|---|
continental_protection | 0.000 | 0.103 | 0.109 | 0.174 | 0.160 | 1.000 |
highseas_protection | 0.000 | 0.000 | 0.000 | 0.068 | 0.000 | 1.000 |
continental_range | 0.000 | 56.000 | 193.000 | 1,248.928 | 650.000 | 40,875.000 |
highseas_range | 0.000 | 0.000 | 0.000 | 805.935 | 0.000 | 63,442.000 |
total_range | 1.000 | 56.000 | 200.000 | 2,054.864 | 655.000 | 104,317.000 |
weighted_protection | 0.100 | 0.103 | 0.111 | 0.177 | 0.168 | 1.000 |
EDGE2 | 0.000 | 0.000 | 0.001 | 0.045 | 0.018 | 1.000 |
Code
# Create visualizations
hist_protect <- ggplot(combined_protection_EDGE2_01, aes(x = weighted_protection)) +
geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
scale_x_continuous(limits=c(0,1)) +
theme_minimal() +
labs(title = "Histogram of Range-Weighted Protection Fraction",
x = "Weighted Protection Fraction",
y = "Count")
hist_EDGE2 <- ggplot(combined_protection_EDGE2_01, aes(x = EDGE2)) +
geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
theme_minimal() +
labs(title = "Histogram of EDGE2 Scores",
x = "EDGE2 Score",
y = "Count")
scatter_plot <- ggplot(combined_protection_EDGE2_01, aes(x = EDGE2, y = weighted_protection)) +
geom_point(alpha = 0.6, color = "darkblue") +
theme_minimal() +
scale_y_continuous(limits=c(0,1)) +
labs(title = "Scatterplot: EDGE2 vs Weighted Protection Fraction",
x = "EDGE2 Score",
y = "Weighted Protection Fraction")
# Create species range type summary
range_type_summary <- combined_protection_EDGE2_01 %>%
summarise(
total_species = n(),
continental_only = sum(highseas_range == 0 & continental_range > 0),
highseas_only = sum(continental_range == 0 & highseas_range > 0),
both_ranges = sum(continental_range > 0 & highseas_range > 0)
) %>%
pivot_longer(everything(),
names_to = "Distribution Type",
values_to = "Number of Species")
# Create and format the flextable
range_type_table <- flextable(range_type_summary) %>%
set_header_labels(
`Distribution Type` = "Distribution Type",
`Number of Species` = "Number of Species"
) %>%
theme_vanilla() %>%
autofit()
# Display the table
range_type_tableDistribution Type | Number of Species |
|---|---|
total_species | 1,005 |
continental_only | 802 |
highseas_only | 5 |
both_ranges | 198 |
Code
# Arrange plots in a grid
grid_plot <- grid.arrange(
hist_protect, hist_EDGE2, scatter_plot,
layout_matrix = rbind(c(1,2), c(3,3)),
widths = c(1, 1),
heights = c(1, 1)
)
Code
# Save the combined protection data
saveRDS(combined_protection_EDGE2_01, file = here::here("Data", "combined_protection_EDGE2_01.rds"))